#!/usr/bin/python
# -*- coding: UTF-8 -*-

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

def full_connected():
    mnist = input_data.read_data_sets("/Users/mill/Documents/pythondoc/tensorflow-example/line/mnist_data/", one_hot=True)

    with tf.variable_scope("data"):
        x=tf.placeholder(tf.float32,[None,784])
        y_true=tf.placeholder(tf.int32,[None,10])

    with tf.variable_scope("fc_model"):
        weight=tf.Variable(tf.random_normal([784,10],mean=0.0,stddev=1.0),name="w")
        bias = tf.Variable(tf.constant(0.0,shape=[10]))
        y_prodict=tf.matmul(x,weight)+bias

    with tf.variable_scope("soft_cross"):
        loss  = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=y_prodict))

    with tf.variable_scope("optimizer"):
        train_op=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

    with tf.variable_scope("acc"):
        equal_list=tf.equal(tf.argmax(y_true,1),tf.argmax(y_prodict,1))
        accuracy=tf.reduce_mean(tf.cast(equal_list,tf.float32))
    init_op=tf.global_variables_initializer()
    with tf.Session() as sess:
        sess.run(init_op)
        for i in range(2000):
            # 取出真实存在的特征值和目标值
            mnist_x,mnist_y=mnist.train.next_batch(50)
            # 运行OP训练
            sess.run(train_op,feed_dict={x:mnist_x,y_true:mnist_y})

            print("训练第%d步,准确率为:%f"%(i,sess.run(accuracy,feed_dict={x:mnist_x,y_true:mnist_y})))
        pass

if __name__ == '__main__':
    full_connected()
